We conduct experiments on two datasets for different tasks.
The first dataset is Adult [1]}https://archive.ics.uci.edu/ml/datasets/adult, which is a widely used benchmark income prediction dataset for fairness-aware machine learning research.
The task is to infer whether the yearly income of a person is higher than $50K and gender is regarded as the sensitive attribute.
There are 32,650 male users and 16,192 female users in total.
The input sample in this dataset can be formulated as a feature vector with both categorical and numerical variables.
The second dataset is the news recommendation dataset used in FairRec [2]}, which contains the news click logs of 10,000 users as well as the observed gender labels of a part of users.
Among users with gender labels, there are 2,484 male users and 1,744 female users.
We denote this dataset as NewsRec.
Gender is the sensitive attribute in this dataset.
Each sample log in this dataset contains a user's historical clicked news, a candidate news article, and the corresponding click label.
Each impression contains a set of candidate news articles displayed to the same user at a certain time.
The statistics of two datasets are listed in Table REF .
The training/test sets are randomly divided on the Adult dataset, while they are partitioned by time on the NewsRec dataset (logs in the last week are used for test).
